{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm.auto import tqdm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mean scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique_id</th>\n",
       "      <th>ds</th>\n",
       "      <th>y</th>\n",
       "      <th>published</th>\n",
       "      <th>is_holiday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>1131</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>1764</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2020-01-03</td>\n",
       "      <td>1699</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>2020-01-04</td>\n",
       "      <td>1322</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>2020-01-05</td>\n",
       "      <td>1491</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   unique_id         ds     y  published  is_holiday\n",
       "0          0 2020-01-01  1131        0.0           1\n",
       "1          0 2020-01-02  1764        0.0           0\n",
       "2          0 2020-01-03  1699        0.0           0\n",
       "3          0 2020-01-04  1322        0.0           0\n",
       "4          0 2020-01-05  1491        0.0           0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('https://raw.githubusercontent.com/marcopeix/time-series-analysis/master/data/medium_views_published_holidays.csv')\n",
    "df['ds'] = pd.to_datetime(df['ds'])\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mean_scaling(x):\n",
    "    mean = np.mean(np.abs(x))\n",
    "\n",
    "    return x/mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = df['y'].values\n",
    "y_scaled = mean_scaling(y)\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(ncols=1, nrows=2)\n",
    "\n",
    "ax1.plot(df['ds'][:200], y[:200], color='blue', label='Original')\n",
    "ax1.set_ylabel('Daily visits')\n",
    "ax1.legend()\n",
    "\n",
    "ax2.plot(df['ds'][:200], y_scaled[:200], color='orange', label='Scaled')\n",
    "ax2.set_ylabel('Daily visits (scaled)')\n",
    "ax2.legend()\n",
    "\n",
    "fig.autofmt_xdate()\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quantization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = pd.qcut(y_scaled, q=100, labels=False)\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(ncols=1, nrows=2)\n",
    "\n",
    "ax1.plot(df['ds'][:100], y_scaled[:100], color='blue', label='Scaled')\n",
    "ax1.set_ylabel('Daily visits (scaled)')\n",
    "ax1.legend()\n",
    "\n",
    "ax2.bar(df['ds'][:100], bins[:100], color='orange', label='Quantized')\n",
    "ax2.set_ylabel('Percentile')\n",
    "ax2.legend()\n",
    "\n",
    "fig.autofmt_xdate()\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Forecasting with Chronos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/miniconda3/envs/medium/lib/python3.10/site-packages/datasetsforecast/m3.py:108: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  freq = pd.tseries.frequencies.to_offset(class_group.freq)\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "from datasetsforecast.m3 import M3\n",
    "from utilsforecast.losses import *\n",
    "from utilsforecast.evaluation import evaluate\n",
    "import torch\n",
    "from chronos import ChronosPipeline\n",
    "\n",
    "Y_df, *_ = M3.load(directory='./', group='Monthly')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3f832fddc1064d47946ddf9de6b004fd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/119 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19.08080816268921\n"
     ]
    }
   ],
   "source": [
    "pipeline = ChronosPipeline.from_pretrained(\n",
    "    \"amazon/chronos-t5-tiny\",\n",
    "    device_map=\"cuda\",\n",
    "    torch_dtype=torch.bfloat16,\n",
    ")\n",
    "\n",
    "horizon = 12\n",
    "batch_size = 12\n",
    "\n",
    "\n",
    "actual = []\n",
    "chronos_tiny_preds = []\n",
    "\n",
    "start = time.time()\n",
    "all_timeseries = [\n",
    "    torch.tensor(sub_df[\"y\"].values[:-horizon])\n",
    "    for _, sub_df in Y_df.groupby(\"unique_id\")\n",
    "]\n",
    "for i in tqdm(range(0, len(all_timeseries), batch_size)):\n",
    "    batch_context = all_timeseries[i : i + batch_size]\n",
    "    forecast = pipeline.predict(batch_context, horizon)\n",
    "    predictions = np.quantile(forecast.numpy(), 0.5, axis=1)\n",
    "\n",
    "    chronos_tiny_preds.append(predictions)\n",
    "\n",
    "chronos_tiny_preds = np.concatenate(chronos_tiny_preds)\n",
    "chronos_tiny_duration = time.time() - start\n",
    "print(chronos_tiny_duration)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5fcef3ae8f7a476ab1d7f156b42182b0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/119 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "138.41768598556519\n"
     ]
    }
   ],
   "source": [
    "pipeline = ChronosPipeline.from_pretrained(\n",
    "    \"amazon/chronos-t5-large\",\n",
    "    device_map=\"cuda\",\n",
    "    torch_dtype=torch.bfloat16,\n",
    ")\n",
    "\n",
    "horizon = 12\n",
    "batch_size = 12\n",
    "\n",
    "\n",
    "actual = []\n",
    "chronos_large_preds = []\n",
    "\n",
    "start = time.time()\n",
    "all_timeseries = [\n",
    "    torch.tensor(sub_df[\"y\"].values[:-horizon])\n",
    "    for _, sub_df in Y_df.groupby(\"unique_id\")\n",
    "]\n",
    "for i in tqdm(range(0, len(all_timeseries), batch_size)):\n",
    "    batch_context = all_timeseries[i : i + batch_size]\n",
    "    forecast = pipeline.predict(batch_context, horizon)\n",
    "    predictions = np.quantile(forecast.numpy(), 0.5, axis=1)\n",
    "\n",
    "    chronos_large_preds.append(predictions)\n",
    "\n",
    "chronos_large_preds = np.concatenate(chronos_large_preds)\n",
    "chronos_large_duration = time.time() - start\n",
    "print(chronos_large_duration)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "rows = {\"unique_id\": [], \"chronos_tiny_pred\": [], \"chronos_large_pred\": []}\n",
    "for (name, _), tiny_pred, large_pred in zip(\n",
    "    Y_df.groupby(\"unique_id\"), chronos_tiny_preds, chronos_large_preds\n",
    "):\n",
    "    rows[\"unique_id\"].extend([name] * horizon)\n",
    "    rows[\"chronos_tiny_pred\"].extend(tiny_pred)\n",
    "    rows[\"chronos_large_pred\"].extend(large_pred)\n",
    "chronos_pred_df = pd.DataFrame(rows)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testing N-BEATS and MLP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:lightning_fabric.utilities.seed:Seed set to 1\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c321751ae06a486382c28353e9f3f330",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Sanity Checking: |          | 0/? [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1cb6861ec4ef46ca9e711164f8209a1e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training: |          | 0/? [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "14164def8c5f4c19bbbd25eaca972b68",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: |          | 0/? [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "13e6bf0ff17946ad8433e1a2e8ca657c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: |          | 0/? [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f217166da9b3426fa36c47c43f3d8a98",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: |          | 0/? [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "25f54a1fd8f547f59bc2d5bc310c5c2c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: |          | 0/? [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "10960f9854744a46a37ccb21dca1ca85",
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       "Validation: |          | 0/? [00:00<?, ?it/s]"
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     },
     "metadata": {},
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    },
    {
     "data": {
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       "model_id": "b197f43dc4c44d7088eb06292f5a5238",
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       "Validation: |          | 0/? [00:00<?, ?it/s]"
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     },
     "metadata": {},
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    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bf4ed63d440f4e7a9c0e1293e4c965a9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: |          | 0/? [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a31c38bf347c4e128c8c6e10899b0cc7",
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       "version_minor": 0
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      "text/plain": [
       "Validation: |          | 0/? [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dfc604abed8c42059bf778f46f9d8049",
       "version_major": 2,
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      },
      "text/plain": [
       "Validation: |          | 0/? [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "65b04348f0b84c67bdc726b5a110d014",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: |          | 0/? [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "184e90234665465e8bb3e49c4da67df9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: |          | 0/? [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23.294083833694458\n"
     ]
    }
   ],
   "source": [
    "from neuralforecast.models import MLP, NBEATS\n",
    "from neuralforecast.losses.pytorch import HuberLoss\n",
    "from neuralforecast.core import NeuralForecast\n",
    "\n",
    "horizon = 12\n",
    "val_size = 12\n",
    "test_size = 12\n",
    "\n",
    "mlp = MLP(h=horizon, input_size=3*horizon, loss=HuberLoss(), devices=1, accelerator='cpu')\n",
    "\n",
    "start = time.time()\n",
    "nf = NeuralForecast(models=[mlp], freq='M')\n",
    "mlp_forecasts_df = nf.cross_validation(df=Y_df, val_size=val_size, test_size=test_size, n_windows=None, verbose=True)\n",
    "\n",
    "mlp_duration = time.time() - start\n",
    "\n",
    "mlp_forecasts_df.head()\n",
    "\n",
    "print(mlp_duration)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:lightning_fabric.utilities.seed:Seed set to 1\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "77469e18877d4f0b98fd1f1940728159",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Sanity Checking: |          | 0/? [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b7565c3e07734ae0807ffc2c4bc53d33",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training: |          | 0/? [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "59af4c4bfc5849b8bfa40cad7e11f683",
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      },
      "text/plain": [
       "Validation: |          | 0/? [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "65773393bef84b58b4bef1355be7f09b",
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       "Validation: |          | 0/? [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2eb3734fd40b4bba93c8e257c6cf727a",
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       "Validation: |          | 0/? [00:00<?, ?it/s]"
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     },
     "metadata": {},
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    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3aeca116692247d8b3a33b881a5debdc",
       "version_major": 2,
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       "Validation: |          | 0/? [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0b8237f4f87d435080470b2007f05d97",
       "version_major": 2,
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      },
      "text/plain": [
       "Validation: |          | 0/? [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f9ddb1c3fa00406fa696057f77d0719c",
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       "Validation: |          | 0/? [00:00<?, ?it/s]"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7780f059600846939c14af5ae4c4ed96",
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       "Validation: |          | 0/? [00:00<?, ?it/s]"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fc55f22c1ba441cba8ec568a628363af",
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       "Validation: |          | 0/? [00:00<?, ?it/s]"
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6ded74913a554810ab2a6ef25df05b6c",
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       "Validation: |          | 0/? [00:00<?, ?it/s]"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "32f8c2861f2b4436b6edd63d7c0bfe79",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: |          | 0/? [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d63e7c1e36c44ff081bd3b9259d60fed",
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       "version_minor": 0
      },
      "text/plain": [
       "Predicting: |          | 0/? [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "49.14376521110535\n"
     ]
    }
   ],
   "source": [
    "nbeats = NBEATS(h=horizon, input_size=3*horizon, loss=HuberLoss(), devices=1, accelerator='cpu')\n",
    "\n",
    "start = time.time()\n",
    "nf = NeuralForecast(models=[nbeats], freq='M')\n",
    "nbeats_forecasts_df = nf.cross_validation(df=Y_df, val_size=val_size, test_size=test_size, n_windows=None, verbose=True)\n",
    "\n",
    "nbeats_duration = time.time() - start\n",
    "\n",
    "nbeats_forecasts_df.head()\n",
    "\n",
    "print(nbeats_duration)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>metric</th>\n",
       "      <th>MLP</th>\n",
       "      <th>NBEATS</th>\n",
       "      <th>Chronos-Tiny</th>\n",
       "      <th>Chronos-Large</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mae</td>\n",
       "      <td>566.162569</td>\n",
       "      <td>551.727285</td>\n",
       "      <td>582.312115</td>\n",
       "      <td>563.345977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>smape</td>\n",
       "      <td>0.064215</td>\n",
       "      <td>0.062675</td>\n",
       "      <td>0.065570</td>\n",
       "      <td>0.063084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Time</td>\n",
       "      <td>23.294084</td>\n",
       "      <td>49.143765</td>\n",
       "      <td>19.080808</td>\n",
       "      <td>138.417686</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  metric         MLP      NBEATS  Chronos-Tiny  Chronos-Large\n",
       "0    mae  566.162569  551.727285    582.312115     563.345977\n",
       "1  smape    0.064215    0.062675      0.065570       0.063084\n",
       "2   Time   23.294084   49.143765     19.080808     138.417686"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from utilsforecast.losses import mae, smape\n",
    "from utilsforecast.evaluation import evaluate\n",
    "\n",
    "forecast_df = mlp_forecasts_df\n",
    "forecast_df[\"NBEATS\"] = nbeats_forecasts_df[\"NBEATS\"]\n",
    "forecast_df[\"Chronos-Tiny\"] = chronos_pred_df[\"chronos_tiny_pred\"]\n",
    "forecast_df[\"Chronos-Large\"] = chronos_pred_df[\"chronos_large_pred\"]\n",
    "\n",
    "evaluation = evaluate(\n",
    "    forecast_df,\n",
    "    metrics=[mae, smape],\n",
    "    models=[\"MLP\", \"NBEATS\", \"Chronos-Tiny\", \"Chronos-Large\"],\n",
    "    target_col=\"y\",\n",
    ")\n",
    "\n",
    "time = pd.DataFrame(\n",
    "    {\n",
    "        \"metric\": [\"Time\"],\n",
    "        \"MLP\": [mlp_duration],\n",
    "        \"NBEATS\": [nbeats_duration],\n",
    "        \"Chronos-Tiny\": [chronos_tiny_duration],\n",
    "        \"Chronos-Large\": [chronos_large_duration],\n",
    "    }\n",
    ")\n",
    "avg_metrics = (\n",
    "    evaluation.drop(columns=\"unique_id\").groupby(\"metric\").mean().reset_index()\n",
    ")\n",
    "avg_metrics = pd.concat([avg_metrics, time]).reset_index(drop=True)\n",
    "\n",
    "avg_metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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